We’ve spent years trying to make our chatbots sound more human, which is great. But what if the larger goal should have also been to make them as useful as possible while we’re at it?
Agility requires more than just adopting the latest technology; it demands a fundamental rethinking of customer engagement, moving from reactive responses to proactive problem-solving.
Today, we’re going to talk about the next evolution of AI in customer service. As more companies turn to automation to manage scale and efficiency, the real challenge isn’t just implementing a chatbot; it’s ensuring that technology actively solves problems and enhances the customer relationship, rather than just deflecting tickets.
To help me discuss this topic, I’d like to welcome, Alan Ranger, VP Marketing at NiCE Cognigy.
About Alan Ranger
Alan Ranger is VP of Marketing at NiCE Cognigy, the global leader in enterprise AI agents. With over 30 years of experience in tech, Alan has led growth strategies at both startups and public software companies. At NiCE Cognigy, he helps organizations adopt AI that delivers real business value reducing costs while improving customer satisfaction. Prior to NiCE Cognigy, Alan led global market development at LivePerson, where he helped double revenues. He now leads NiCE Cognigy’s expansion in the US and UK and works closely with clients to deploy AI that scales.
Alan Ranger on LinkedIn: https://www.linkedin.com/in/aranger/
Resources
NiCE Cognigy: https://www.cognigy.com
Take your personal data back with Incogni! Use code AGILE at the link below and get 60% off an annual plan: https://incogni.com/agile
The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow
Catch the future of e-commerce at eTail Palm Springs, Feb 23-26 in Palm Springs, CA. Go here for more details: https://etailwest.wbresearch.com/
Drive your customers to new horizons at the premier retail event of the year for Retail and Brand marketers. Learn more at CRMC 2026, June 1-3. https://www.thecrmc.com/
Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://advertalize.com/r/faaed112fc9887f3
Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom
Don’t miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show
Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com
The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company
Transcript
[ 0:06 ] Greg Kihlstrom: We’ve spent years trying to make our chatbots sound more human, which is great, but what if the larger goal should have also been to make them as useful as possible while we’re at it? Agility requires more than just adopting the latest technology. It demands a fundamental rethinking of customer engagement, moving from reactive responses to proactive problem solving. Today, we’re going to talk about the next evolution of AI and customer service. As more companies turn to automation to manage scale and efficiency, the real challenge isn’t just implementing a chatbot, it’s ensuring the technology actively solves problems and enhances the customer relationship, rather than just deflecting tickets. To help me discuss this topic, I’d like to welcome Alan Ranger, VP Marketing at Nice Cognigy. Alan, welcome to the show.
[ 1:35 ] Alan Ranger: Thanks, Greg. It’s great to be here.
[ 1:36 ] Greg Kihlstrom: Yeah, looking forward to talking about this topic with you. Definitely a top lots of people’s minds. Before we dive in though, why don’t you give a little background on yourself and your role?
[ 1:45 ] Alan Ranger: Yeah, sure, I’d be delighted to. So, yeah, I’m lucky enough to lead marketing at Cognigy, which is now Nice Cognigy. So, I joined three years ago and have taken the company through the Series C and then the acquisition by Nice, which was in September of 2025.
[ 2:00 ] Greg Kihlstrom: Nice, nice. Love it. So, yeah, let’s, let’s dive in and we’ll, we’ll start with the the strategic view here. And, you know, as I teed up in the intro, talking about this idea of certainly we want automation and there’s lots of benefits to automation, but, you know, the, the idea of of AI and customer service isn’t even a new thing, but the use of agentic AI certainly represents a significant leap beyond the typical chatbot experience that I’m sure we’ve all had. So, from the strategic lens, what’s the fundamental business shift that makes this kind of proactive, action-oriented AI not just a nice to have but now a necessity for large brands?
[ 2:39 ] Alan Ranger: That’s it. You know, the, the whole sort of meaning behind agentic is that these AI’s have agency to actually do something. So, going back to your introduction, it’s all about completing tasks and doing it properly. And, you know, it’s relatively easy these days to take a large language model and put a wrapper around it and create a really good conversational chatbot, but it might not do anything. You know, it might not be integrated to the, the back end of the enterprise systems. Uh, it may not have been, you know, given the guard rails of how to operate in an enterprise environment. So, I think it’s one of those things that we did an awful lot with AI in customer service before, with the more sort of traditional, as it’s now called, the deterministic flow-based AI, and you could achieve great things. I mean, one of our major customers is Lufthansa, the German airline. And the stuff they did with flow-based deterministic AI was remarkable. Everything from rebooking flights to taking recommendations, inquiries, all that sort of stuff. It worked really, really well. But what we were so surprised with when we launched the agentic was that they just took it to the next level. And it’s the, the, the AI’s ability to reason almost like a human, and to be able to have specific tasks it’s going to complete and just do that at scale in a way that’s really good from customer experience perspective.
[ 3:49 ] Greg Kihlstrom: Yeah, yeah. And so, when leaders are certainly, there’s a lot of leaders already either considering this or or actively adopting it. When they’re, when they’re doing this, there’s often some internal resistance, right? I mean, as you mentioned, these agentic tools, they’re, they’re good at what they do and and they, they can certainly do some things that humans, you know, formerly were doing or even are still doing in, in some capacities. So there’s, you know, there’s a natural fear of being replaced. Um, but, you know, how do you advise leaders to frame this integration as not really replacement, but as a force multiplier?
[ 4:26 ] Alan Ranger: That’s it. I mean, it’s just about automating the task that humans never should have done. You know, if you’re a human advisor, you shouldn’t be sitting there resetting passwords or or looking up account balances. Anything like that should be automated, and you should really think about using the human advisors as being your brand ambassadors and doing the work of highest value. There’s some industries, obviously, like financial services where, you know, it’s regulated and you can’t have an AI providing financial advice. It has to be a regulated human being that’s been certified and is accountable to financial services authorities and that sort of thing. So, we will never see the the end of human advisors. I think that they will be involved in the higher value stuff. working in partnership with the AI. You know, most of the conversations that our agentic AI is now having, not all of it is entirely autonomous. There’s an awful lot of it where it goes backwards and forwards to a human. And then the AI can even change roles and go from being consumer facing to being agent facing and work in the background, just making sure that they’re given the prompts and the help and the information they need to have a fantastic conversation. And it just lets the human focus on having a great conversation, which is what human advisors are really good at.
[ 5:29 ] Greg Kihlstrom: Yeah. Well, and and it also, um, it gives them more time, right? I mean, if they’re not to your point resetting passwords, even if it’s a relatively quick process, that takes time and it takes time away from solving a nuanced and perhaps, you know, sticky situation that AI may not be adept at handling, right? So, I mean, it’s it’s actually elevating that role, right?
[ 5:51 ] Alan Ranger: Yeah, that’s it. And to be honest, none of our customers have actually laid off human advisors. Uh, they’re all suffering from a huge shortage of of great advisors. Uh, since the pandemic, it’s almost been that nobody wanted to go back to the contact center. Uh, you know, they all found different types of employment. And so, yeah, typically, you know, churn is high still, uh, recruitment is a constant battle. And so what they’re doing is filling the gap, because, you know, unfortunately, whole times have gone up despite all of this new technology that’s available. The availability of the humans has gone down. And it certainly can’t cope with sort of peak transactions. So, you know, going back to our friends at Lufthansa, you know, they have a snowstorm and an airport shut. All of a sudden, 10,000 people called to rebook their flights. You can’t predict that, you can’t scale up with humans. So, you need an automation that’s going to take those peak things, take over all of the the out-of-hours stuff, and also take over some of the multilingual capabilities. So, you don’t need a multilingual contact center anymore. You can have real-time simultaneous translation happening, uh, even if it’s human to human, rather than, uh, human to AI agent.
[ 6:50 ] Greg Kihlstrom: Yeah, and I I think that’s a good part of the conversation to have is that, yeah, it’s exactly what you said, there’s, there’s roles that humans aren’t able to, there’s not enough humans to fill the roles and and and things like that. And I think, you know, that that can often get overlooked in, in some of those, some of those conversations as well.
[ 7:08 ] I guess to to make it more practical to to those that are either in that consideration stage or just, uh, you know, trying to think of how this could look at at some point in the future. Can you walk us through a a practical real-world example, you know, how does agentic AI interaction, you know, fundamentally differ from, you know, a more standard chatbot journey, you know, taking, you know, complex issues into into play?
[ 7:36 ] Alan Ranger: Yeah. So, with the traditional AI, the deterministic one, it was, you know, very good at construct or being constructed to to solve very simple tasks. So, you know, as I said, the the flight rebooking one is a perfect one. There’s not too many things that can go wrong or deviations on the path where and you can build a really good deterministic flow-based one that just resolves it at scale, does the job. But as soon as anybody were to then go off the path and maybe, oh, I’ve rebooked the flight but now I need to change the number of passengers, or can you tell me what my allowance is? Can I bring a pet? All this sort of thing. The deterministic one is only built to follow the flow and can only do that and will just have to I don’t understand, which is the most frustrating thing in the world to be talking to an automation. It’s happened to all of us and it continues to happen and it shouldn’t. Now with the agentic stuff, because it’s using the large language model, it understands absolutely everything that’s being said to it. And if it’s able to, it can then resolve the issue that the consumer is calling with. If they can’t resolve it, it can just say, look, I’ve not been trained on this. Let me pass you over to my other agentic AI who has been trained on this. Or maybe I’ll pass you on to a human being. So a great example is one of the world’s largest bus companies, or coach lines company. I unfortunately can’t say the name, but I think people will guess it. Anyway, they, they did their first AI automation using the deterministic stuff. It took them about six months. But they picked India as their launch market, which has got to be one of the world’s most difficult places for coach travel. You know, the, the road system is chaotic, you never really know what’s going to happen. And they built a really good deterministic bot that that actually resolved all the things like timetabling, where’s my bus, ticket buying, all that sort of thing. Very high adoption rates. They then moved on with the same technology to launch in the US. This was probably about eight, nine months ago. And it failed acceptance testing because it wasn’t good enough in terms of experience. So within a week, they rebuilt using agentic technologies and launched. And it’s gone down an absolute storm. And the reason they were able to do that, I think it’s because they had the experience of building the deterministic bot. And they knew how to do the integration into the backend system. So it was enterprise ready. It was on a proven scalable platform. And then when they launched, it was just understanding everything and using its own capabilities to to answer questions. There was one person, for example, had called in and said, oh, I’m buying a ticket for my friend, but they’ve lost their mobile phone. And so it automatically, that’s fine, you can just print it out. And we hadn’t trained it on, you can just print, knew the knowledge of the world that this was a solution. So it’s actually solving problems. But and only focusing on the problems it’s allowed. Had you asked it, you know, who’s going to be the next president of the USA? It would go, that’s very interesting, but I’ve not been trained on that and I can’t give you an answer.
[ 10:10 ] Greg Kihlstrom: Right, right. Yeah. Well, and I I think, you know, to your point, that for the company to have been through the process of of doing it in a more, I’ll just call it, you know, deterministic, to use your word, or a more manual way of of of routing these if this then that kind of of kind of process, it it does seem valuable to have gone through that exercise, almost like a flow chart exercise before you do the generative AI or the LLM based version, just so you have a kind of knowledge of of overall things. But obviously, one of the big components here that really helps it is also having access to data, right? And so, how does, you know, how, how do you recommend that an organization think about, you know, maybe having mapped it out for the for the deterministic model helps them understand some of the processes, but what about the data component, you know, what should a company prepare, um, as they as they embark on this?
[ 11:06 ] Alan Ranger: Yeah, the the data component is huge. I mean, the our friends over Gartner have put out this is probably again about 10 months ago, a Doom and Gloom report saying 95% of AI projects fail. And it’s pretty much because the data underneath isn’t correct. I mean, you can consume any data now and, you know, convert it in a way so you can have a conversation. So, anything from a book to a PDF to, you know, the FAQs, they can all be consumed into the knowledge base, and then, you know, using agentic capabilities, it can just be questioned in a very human-like way. But if that data is out of date or wrong, then yes, that’s a huge issue. And also, you need to make sure that when you build the AI agent, it’s grounded only on the data you give it. You can’t allow it to use its knowledge of the rest of the world to have the conversation. It has to be completely anchored and grounded on on the data. So, yeah, the the key thing is really to if you have had experience of building deterministic, everybody knows the the value in structured data that that’s correct. It doesn’t need to be so structured with agentic. It just needs to be up to date and there needs to be a process to make sure the latest thing is up to date. I mean, typically, when an AI goes wrong, it’s because the data underneath has been incorrect. I mean, it was before the agentic version, there’s the famous Canadian airline that promised a refund because the data was wrong. It wasn’t anything, it wasn’t hallucinating, it wasn’t making stuff up, it wasn’t even, you know, a large language model. It was traditional deterministic one. But the data just hadn’t been updated. They’d changed the policy and nobody told the AI.
[ 12:34 ] Greg Kihlstrom: Yeah, yeah. That’s, yeah, that’s a for the for those listening that that aren’t familiar with that story, that’s definitely a a case study to, um, to pay attention to, yeah.
[ 12:49 ] So, let’s talk about measurement here as well. And so, certainly classic, you know, customer experience metrics are still going to be in play, you know, if you use CSAT, NPS, average handle time, things like that. Are there other measurements that also either need to be added or maybe become more important when agentic AI is introduced into the mix?
[ 13:12 ] Alan Ranger: Yeah, I mean, what we’re seeing more and more is people measuring by outcome. So, they’ve actually thrown away all of the traditional measurements, because they were there for the measurement and performance management of human advisors. Uh, when you’ve got, you know, AI agents that have unlimited capacity and scalability and are available 24/7, a lot of the metrics go away. So, the classic one is average handling time. It really doesn’t matter how anymore, how long it takes because it doesn’t cost any more to have an AI agent having a 10-minute conversation as it does, you know, having a 30-second one. Uh, and equally, if you’re measuring the humans, then you probably want to get rid of average handling time anyway because they’ll only be taking the more complex cases and the highest value cases, and they will take longer. So, yeah, what we’re seeing is a lot of people actually measuring it on, you know, percentage of task completed fully automated end to end. Those that aren’t, but another thing we’re seeing more and more is a huge sort of focus on CSAT and customer experience. Everybody’s now seeing that’s the differentiator. And many people are actually going for that, rather than operational sort of cost savings, it’s much more about CSAT. Particularly, as we’re moving outside the the inbound contact center. We’re now seeing increasingly that they’re using agentic capabilities to do outbound sales and marketing. It’s something you could never have done with a deterministic flow because you can’t tell what somebody’s going to say when you call them or message them. But with the agentic, it understands the whole conversation. It’s been given the task and yeah, we’re we’re seeing more and more people now doing sort of outbound stuff. And again, a lot of that is measured on on customer satisfaction and the the repeatability of business and and the loyalty that they’re gaining.
[ 14:44 ] Greg Kihlstrom: Yeah, and I mean that that makes a lot of sense because, I mean, certainly, you know, consumers are, they’re time challenged like we all are, and so they want time back in their day and stuff. But, I mean, I’m sure there’s a statistic out there that supports this. I I think I’ve seen a few things, but, you know, if it takes five extra seconds to deal with an issue and it’s actually resolved and I don’t have to go back and ever talk to that company about that issue again, I’m going to be way more happy than it taking 10 seconds and I’m off the, you know, off the chat, but my problem isn’t resolved. So, to your point, average handle time is probably directional, but it’s not, it’s not the end all be all.
[ 15:25 ] Alan Ranger: The one thing it gets rid of completely is hold time, though, which is critical. Nobody likes to be put on hold.
[ 15:31 ] Greg Kihlstrom: Yeah. Yeah, yeah. I mean, think about that. Yeah, definitely. And so, to, you know, to your to your point about the the upselling or cross-selling or just the, the, the, the commerce part of this too, because customers also want good opportunities and offers and things like, you know, it’s it doesn’t always have to feel like they’re being sold and and and things like that. So, you know, this also gets into both the loyalty aspect of, okay, my problems got resolved in a in a timely manner and in the right way, but also, you know, customer lifetime value increases if we’re also able to upsell, cross-sell and and add real true value to the customer relationship. How do you work with an organization to kind of paint this fuller picture of, you know, yes, we’re solving the traditional customer service challenges, but, hey, there’s this opportunity you already mentioned, which is we also can present new opportunities to them.
[ 16:31 ] Alan Ranger: And, you know, we’re in no way done with the inbound customer service. You know, it’s still pretty terrible in most organizations. So, there’s an awful lot still to be done there. But no, then we’ll, we’ll sort of paint the bigger picture of looking at some of their their outbound and look for some high value, high volume automations that we can bring in. So, a great example is one of Europe’s largest banks. Whenever they sell a financial services product, um, they need to basically have a conversation with a regulated human advisor. So, what happens is somebody fills a form in on the website saying they want to take out a personal loan to to buy a car or do some home improvements. And then they need to have a quick chat with a financial advisor to make sure that the right product is sold. And typically, what was happening is these financial advisors were given a big list of leads in the morning of the people that had filled in their, um, the application forms. And they were just outbound calling. And really, they only got a 20% pickup of people they wanted to talk to. So, really expensive, highly qualified, you know, regulated individuals making these outbound calls with a 20% pickup. So, what we did was built them a pretty simple agentic AI outbound caller. And it was called, it would call through the list to say, you know, hi, Greg, I’m just calling about the application you made, um, three weeks ago for for the bank. You’ll just need to have five minutes with one of our advisors, is now a good time. And because it was hyper personalized, you know, you were expecting the call because you’d filled in the form. And if you said, no, now isn’t a good time, then it would reschedule. And then it would call back. And if it still wasn’t a good time, it would reschedule and it would call back. It wouldn’t get frustrated, it would be very polite and very personal and all that sort of thing. And then eventually, when the the person who’d filled in the form was ready to speak, they’d be transferred to the human advisor who would then complete it. And that then resulted in 80% of the people that the advisors were talking to, actually completed the loan and took out the financial product. So, that’s a huge value for them, you know, in terms of making them more efficient. The advisors loved it because they were all on commission. So, they weren’t wasting their time, you know, having dead end calls. They were only talking to people that they wanted and, uh, another customer, again, in sales marketing, it’s a subscription fashion brand and you pay your subscription. So, they built a retention automation that when you wanted to cancel your subscription, it would talk to you and work out how to retain you. You know, it would understand when to make an offer of, well, how about you have a payment holiday for two months and this sort of thing. And within six weeks of launching, it was outperforming the humans that used to do the task of retention. So, yeah, it’s all that side of thing. So, you used to pick those big pictures. And then for them those brands that typically haven’t had a relationship with their consumers. So, the fast moving consumer goods, say a shampoo. You know, a shampoo manufacturer has no relationship with the user of its shampoo. Its only relationship is with the retailer and then the consumer maybe has a relationship with the retailer. But these days, you know, you can have put a QR code on the back of the shampoo bottle. Somebody uses it and it makes their hair go frizzy. So, they scan the bottle with their phone, start a WhatsApp conversation directly with an automation of the brand. The automation knows which product you’re looking at because of the QR code and you’ve said it made your hair go frizzy, so it knows what the problem is. And it can work with you to then identify which shampoo you should be using. And the great thing is, that then creates a lifetime relationship on your messaging because unless you as the user delete it, it persists. And then maybe in three weeks time if the permissions have been given, the shampoo company can go, hey, Greg, how’s the frizzy hair? I’ve got this brilliant new conditioning product that you would love for your hair. Here’s a voucher for 50% off. Show this on your phone to the retailer. And that that way, they’re actually, it’s the marketer’s dream. It’s a personalized one-to-one relationship with every single one of your consumers. And I think that’s really going to be where the future’s going to take us. It’s going to go way beyond the the front office of the contact center, right the way through the middle and back office and every relationship that every consumer has with the brand can be enabled through conversational AI.
[ 20:15 ] Greg Kihlstrom: Yeah, yeah. I I agreed. And I think thinking about the future, what really struck me, lots of, you know, I was keeping up with the holiday sales retail numbers and everything like that, and, you know, certainly there’s there’s other things to look at there. But what really struck me was just consumers’ comfortability with using AI tools. And so, you know, to bring that into this conversation, you know, I think certainly, again, we’ve all been, we’ve just like there’s the phone tree Doom Loop kind of thing you can get into. We’ve all been on those chatbot conversations that go nowhere as well. But I think humans are a lot, are a lot more comfortable interacting with AI, and they probably will continue to do that. And they’re going to have their own agents. Yeah. If they don’t already, you know, a handful of people already are doing that, I’m sure, but that’s going to become more and more commonplace. Where do you see things going over the next, you know, three to five years here of how does customer service evolve when, you know, A, you know, consumers are more comfortable with AI, consumers have their own agents, companies have their own agents, you know, how does how does all this kind of evolve?
[ 21:25 ] Alan Ranger: Um, I I think the first thing will be in customer service itself. We’ll see pretty much self-complete automations. So, you know, we’ll be able to take all of the data from the human advisor conversations, all of the data from the the automated ones, and we’ll be able to work out which are the most popular intents and then automatically build an AI automation using the agentic capabilities to actually resolve those issues. So, we’ll get to the point where I don’t think there’s pretty much any customer service inbound use case that can’t be solved and automated pretty much end to end. So, I think that will then result in, you know, a much better customer service, get rid of all the whole times. Pretty much everything you need to do when you interact with a brand can be handled by AI. But what I think we’ll also see is a complete change in the way that we browse and get information. For a start, everybody will use generative search rather than traditional sort of web search. But what we’ll see is that we’ll have conversational websites. So, you’ll start with a big chat window and you’ll you’ll ask about something and the whole window will change based on your conversation. So, it might be that you’re looking to upgrade your mobile phone. So, the the offers as you speak to the, um, the AI agent will change in the window. And so it won’t be a fixed website anymore where you’re just navigate around with clicks and, you know, the odd thing here. It will actually dynamically respond to the conversation you have and that’ll just be an amazing experience. We’re we’re piloting this at the moment and I I really think that’s going to be the future. Definitely within five years, but maybe within, you know, the the next couple of years.
[ 22:51 ] Greg Kihlstrom: Nice, nice. Love that. So, one one thing, and certainly I that the automation and all of that sounds sounds amazing. How do we keep this also feeling not too automated, not too inhuman, but also, you know, keep the keep a human aspect to all of this as well? Like what’s what’s the right balance to play knowing that, you know, again, consumers, they want, they want speed and efficiency, but they also want to feel seen and heard.
[ 23:21 ] Alan Ranger: Yeah. There there will always be human oversight and there will always be the capability to be transferred to a human advisor. And sometimes there may be a human in the loop without even them actually having to connect with the consumer. I mean, that that fashion brand I was talking about, they they have a returns policy. And so you take a photograph of of the the product. And then the AI tries to work out whether it’s a warranty claim or whether actually you’ve damaged it. So, it might be a broken zip on a jacket, something like that. And if the AI can’t work it out, it actually sends a Teams message to a human in the background with a picture and says, hey, I can’t understand this. And the human can go either, yeah, that’s a warranty or no, that’s damaged. So, that will always so people know that they’ve got the human with the oversight in the background. And and certainly in Europe and places like that, you know, there’s regulations that insist there is human oversight. And I think if it’s something, you know, like medical or if it’s financial services, all the regulated industries will still have, you know, humans directly talking to other humans to to resolve their problems. So, I think we’ll keep that. But also, with the way that we now build the agentic AI, you you literally describe its personality and you can adjust its tone and even its accent in in terms of the way that it interacts. And, you know, it can have sort of, I suppose fake empathy. It can never have real empathy because it’s still a cold-hearted machine. But it can understand the sentiment of the call that’s you know, that’s going on or the messaging conversation and adapt in a way that a human will as well. And actually just, you know, provide that that that human touch, but, you know, just at scale.
[ 24:43 ] Greg Kihlstrom: Yeah. Yeah. Well, and I I mean, I think there are websites or mobile apps that were designed with empathy, right? So, I I don’t think it’s that far-fetched to say that AI could have, you know, it’s it’s not exactly the same as talking with a human, but it’s also, again, designed with with empathy. And so, the the human that’s experiencing it will feel that, will feel that, right?
[ 25:05 ] Alan Ranger: Yeah, yeah, it can even understand sarcasm. I mean, you know, for the classic example of, you know, you’re an airline and you get a message back saying, uh, thanks for the upgrade, positive sentiment. And then somebody might equally say, thanks for losing my luggage. And with the old system, that would be a positive sentiment, but the AI understands that it’s not because it understands the context of what’s been said. It understands that the airline has lost your luggage and you’re not happy.
[ 25:29 ] Greg Kihlstrom: Right, right. Absolutely. Well, Alan, thanks so much for joining today. I got a couple couple questions for you as we wrap up here. First one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
[ 25:44 ] Alan Ranger: Um, I think we’d be talking about what the human is still doing in the outbound customer service. You know, which of the tasks do we still need humans? And which ones haven’t we automated? I hope we’re not, but we’re probably still going to be talking about why some customer service still sucks. And why people have been resistant. Hopefully, you know, because the technology is now there, it’s now proven and the the adoption rate is remarkable. It’s it’s quicker than I’ve ever seen in my great big long sort of like 30-year career. So, I I think we will see that. But hopefully, we won’t be talking about what’s not been automated. We’ll be celebrating the success and maybe we’ll even be laughing about the old metrics like average handling time and whole times and that sort of thing.
[ 26:19 ] Greg Kihlstrom: Right, right. Yeah. Well, um, last question for you. What do you do to stay agile in your role and how do you find a way to do it consistently?
[ 26:27 ] Alan Ranger: All right. Oh, so, at the moment, I’m in Florida. I’m at CCW, the event here. So, what I do is I spend as much as time as I can either presenting on stage or just joining in the forums and communities of the people whose, um, issues we’re solving. And I find that, you know, there’s a lot of geographic differences and cultural differences. So, I try and, you know, get over to the US at least once a month, all over Europe. I’m now expanding into APAC. So, it’s just being with people and hearing what they’re they’ve been told, helping them with the education, and just becoming a trusted peer and part of the communities that everybody’s in. Yeah, that keeps me excited every day when I wake up. I love meeting people and just hearing what’s going on and just being able to use that to make sure that we’re shaping the product in the right direction.
